摘要：【中文】本发明提供一种一致性级别可控的自适应数据同步方法和系统，本方法包括接收集群中节点基本特征与场景特征；计算节点失效概率分布和用户请求概率分布，并与节点基本特征与场景特征一同送入至环境特征模型，结合排除规则集合获取算法的选择范围；根据算法的选择范围与场景特征，结合收益规则集合确定算法以及算法参数，并生成算法决策并同步到每个节点；每个节点根据接收到的算法决策确定算法以及算法参数，以响应用户请求并实现数据同步。本发明解决了大规模集群的数据同步过程中针对不同类型数据的时效性需求，增强了数据同步的性能，保障了水平扩展能力且适用于大规模集群，提供了动态调整和插拔的能力。 【EN】The invention provides a consistency level controllable self-adaptive data synchronization method and a system, wherein the method comprises the steps of receiving basic characteristics and scene characteristics of nodes in a cluster; calculating node failure probability distribution and user request probability distribution, sending the node failure probability distribution and the user request probability distribution to an environment feature model together with the basic features of the nodes and the scene features, and acquiring a selection range of an algorithm by combining an exclusion rule set; determining an algorithm and algorithm parameters by combining a profit rule set according to the selection range and the scene characteristics of the algorithm, generating an algorithm decision and synchronizing the algorithm decision to each node; and each node determines an algorithm and algorithm parameters according to the received algorithm decision so as to respond to the user request and realize data synchronization. The invention solves the timeliness requirement aiming at different types of data in the data synchronization process of the large-scale cluster, enhances the data synchronization performance, ensures the horizontal expansion capability, is suitable for the large-scale cluster, and provides the capabilities of dynamic adjustment and plugging.

摘要：【中文】本发明公开了一种实现智能虚拟数字动物的可学习能力模型个性化的训练学习方法与系统。该方法包括：在云端生成智能虚拟数字动物的程序实例；为智能虚拟数字动物云端实例构造生产动物机器人实物映射，并派发给用户，动物机器人从云端下载相应的可学习能力模型；用户与动物机器人实物进行自然场景交互，动物机器人自动采集、生成个性化训练数据、训练更新可学习能力模型副本并上传到云端，云端更新相应虚拟数字动物的可学习能力模型。本发明有效地解决了现有人工智能模型训练模式对普通人门槛过高、无法大范围发挥大众劳动力的价值，不能参与到虚拟数字智能体能力模型的个性化训练中、和用户交互真实场景缺失等问题。 【EN】The invention discloses a training and learning method and a training and learning system for realizing the individuation of a learnable ability model of an intelligent virtual digital animal. The method comprises the following steps: generating a program example of the intelligent virtual digital animal at the cloud end; constructing a real object mapping of the production animal robot for the cloud example of the intelligent virtual digital animal, and sending the real object mapping to a user, wherein the animal robot downloads a corresponding learnable capability model from the cloud; the method comprises the following steps that a user carries out natural scene interaction with an animal robot object, the animal robot automatically collects, generates personalized training data, trains and updates a learnable ability model copy and uploads the learnable ability model copy to the cloud, and the cloud updates the learnable ability model of the corresponding virtual digital animal. The invention effectively solves the problems that the existing artificial intelligence model training mode has too high threshold for common people, cannot exert the value of mass labor force in a large range, cannot participate in the personalized training of the virtual digital intelligence capability model, has no real scene interaction with users and the like.

摘要：【中文】本发明提供了一种虚拟卫星、基于虚拟卫星的软件与算法的测试平台及方法，涉及虚拟卫星技术领域，该平台包括：卫星控制与状态监视工具对虚拟卫星进行测控和软件/算法的注入，并接收虚拟卫星下传数据；内部测试工具对软件/算法的行为进行监视，并对软件/算法进行性能测试；外部测试工具对软件/算法进行配置项测试和/或系统测试。其中虚拟卫星以数据中心作为接口将云计算平台模拟器与卫星平台模拟器、载荷模拟器解耦。本发明通过虚拟卫星可以提高模拟器的集成化程度；通过监视工具及测试工具实现高效共享及复用测试资源，解决现有卫星测试中存在的无法满足多地域协同、分布式执行、大规模并行测试的问题。 【EN】The invention provides a virtual satellite, a test platform and a method based on software and algorithm of the virtual satellite, relating to the technical field of the virtual satellite, wherein the platform comprises: the satellite control and state monitoring tool carries out measurement and control and software/algorithm injection on the virtual satellite and receives the data downloaded by the virtual satellite; the internal testing tool monitors the behavior of the software/algorithm and tests the performance of the software/algorithm; the external test tool performs configuration item testing and/or system testing on the software/algorithm. The virtual satellite decouples the cloud computing platform simulator with the satellite platform simulator and the load simulator by taking the data center as an interface. The invention can improve the integration degree of the simulator through the virtual satellite; the monitoring tool and the testing tool realize efficient sharing and multiplexing of testing resources, and the problem that the existing satellite testing cannot meet multi-region cooperation, distributed execution and large-scale parallel testing is solved.

摘要：【中文】本发明提供了一种星载软件的测试系统，涉及软件测试的技术领域，包括：虚拟卫星，桌面卫星，虚拟在轨卫星和数据处理装置，其中，虚拟卫星用于对待测试星载软件进行代码测试；桌面卫星用于在虚拟卫星对待测试星载软件进行代码测试之后，对待测试星载软件进行仿真测试；虚拟在轨卫星用于在桌面卫星对待测试星载软件进行代码测试之后，对待测试星载软件进行系统测试；数据处理装置用于在虚拟在轨卫星对待测试星载软件进行代码测试之后，将待测试星载软件发送至软件定义卫星中进行在轨测试，解决了现有的星载软件的测试层次架构单一，无法满足不同的软件测试需求技术问题。 【EN】The invention provides a test system of satellite-borne software, which relates to the technical field of software test and comprises the following steps: the system comprises a virtual satellite, a desktop satellite, a virtual in-orbit satellite and a data processing device, wherein the virtual satellite is used for carrying out code test on satellite-borne software to be tested; the desktop satellite is used for performing simulation test on the to-be-tested satellite-borne software after the virtual satellite performs code test on the to-be-tested satellite-borne software; the virtual in-orbit satellite is used for carrying out system test on the satellite-borne software to be tested after the desktop satellite carries out code test on the satellite-borne software to be tested; the data processing device is used for sending the satellite-borne software to be tested to the software defined satellite for on-orbit testing after the code of the satellite-borne software to be tested is tested by the virtual on-orbit satellite, and the technical problems that the existing satellite-borne software is single in testing level architecture and cannot meet different software testing requirements are solved.

摘要：【中文】本发明公开一种众核平台上面向规则网格问题的结构化着色方法，包括如下步骤：步骤一、依赖特性分析：对按照stencil模板格式生成的计算问题中的各计算任务之间的依赖特性进行分析，包括依赖传递特性分析和强弱依赖方向分析；步骤二、参数搜索：根据依赖特性分析结果，使用四种参数搜索模式进行最优的着色规则参数搜索；步骤三、计算任务划分：根据最优的着色规则参数为每个计算任务着色，并将相同颜色的计算任务分为一组，不同颜色的计算任务分为不同组，总的颜色数即为计算任务的总的划分组数，完成计算任务的划分；本发明保证了在相同颜色数的限制下，保留更多的核心依赖关系，从而有效减少了计算的整体迭代次数，并提高了着色质量。 【EN】The invention discloses a structured coloring method for a many-core platform facing to a regular grid problem, which comprises the following steps: step one, dependence characteristic analysis: analyzing the dependency characteristics among all the calculation tasks in the calculation problem generated according to the STENCIL template format, wherein the dependency characteristics include dependency transfer characteristic analysis and strong and weak dependency direction analysis; step two, parameter searching: performing optimal coloring rule parameter search by using four parameter search modes according to the dependence characteristic analysis result; step three, dividing calculation tasks: coloring each calculation task according to the optimal coloring rule parameters, dividing the calculation tasks with the same color into a group, dividing the calculation tasks with different colors into different groups, wherein the total color number is the total division group number of the calculation tasks, and completing the division of the calculation tasks; the invention ensures that more core dependency relationships are reserved under the limit of the same color number, thereby effectively reducing the overall iteration times of calculation and improving the coloring quality.

摘要：【中文】本发明提供了一种基于软件定义卫星的软件测试床，涉及软件测试的技术领域，包括：开发云，测试云，服务云和测控云，开发云用于获取软件开发者上传的待测试软件，并按照第一测试方式对待测试软件进行软件测试；测试云用于获取开发云测试之后的待测试软件，并按照第二测试方式对开发云测试之后的待测试软件进行测试；测控云用于获取测试云测试之后的待测试软件，将待测试软件上注给软件定义卫星，以及向软件定义卫星发送接收到的目标指令，并获取软件定义卫星基于目标指令反馈的目标数据；服务云用于将目标指令发送给测控云，使测控云向软件定义卫星转发目标指令，解决了现有技术中难以对需要安装在软件定义卫星中的软件进行软件测试的技术问题。 【EN】The invention provides a software testing bed based on a software defined satellite, which relates to the technical field of software testing and comprises the following components: the development cloud is used for acquiring software to be tested uploaded by a software developer and carrying out software testing on the software to be tested according to a first testing mode; the test cloud is used for acquiring the software to be tested after the cloud test is developed and testing the software to be tested after the cloud test is developed according to a second test mode; the measurement and control cloud is used for acquiring software to be tested after the test cloud tests, injecting the software to be tested to the software-defined satellite, sending a received target instruction to the software-defined satellite, and acquiring target data fed back by the software-defined satellite based on the target instruction; the service cloud is used for sending the target instruction to the measurement and control cloud, so that the measurement and control cloud forwards the target instruction to the software-defined satellite, and the technical problem that software which needs to be installed in the software-defined satellite is difficult to test in the prior art is solved.

摘要：【中文】本发明公开了一种基于多种流程模型的资源调度方法和系统。本方法为：1)建立矿业生产资源模型，用于描述各生产资源的资源基本特征、功能性特征、约束性特征；生产资源包括流程资源、离散资源和批量资源；2)建立生产业务模型，包括流程型业务模型、离散型业务模型和批量型业务模型；3)智能调度模块以离散型业务模型为基础，计算不同位置分布上的约束条件；4)智能调度模块根据各位置的约束条件和位置节点的上下游节点信息，规划路径并计算路径上的能量需求和最大吞吐量；5)智能调度模块依据批量型业务模型计算在时序分段期内批量型业务产出，以最大吞吐量为制约条件，选择最小能量需求为调度最佳规划路线。本发明可实现井下调度最优化。 【EN】The invention discloses a resource scheduling method and system based on multiple process models. The method comprises the following steps: 1) establishing a mining production resource model for describing resource basic characteristics, functional characteristics and constraint characteristics of each production resource; the production resources comprise flow resources, discrete resources and batch resources; 2) establishing a production business model which comprises a flow type business model, a discrete type business model and a batch type business model; 3) the intelligent scheduling module calculates constraint conditions on different position distributions on the basis of a discrete service model; 4) the intelligent scheduling module plans a path and calculates the energy requirement and the maximum throughput on the path according to the constraint conditions of each position and the upstream and downstream node information of the position node; 5) the intelligent scheduling module calculates the batch type service output in the time sequence segmentation period according to the batch type service model, and selects the minimum energy requirement as the optimal scheduling route by taking the maximum throughput as a constraint condition. The invention can realize the optimization of underground scheduling.

摘要：【中文】本发明公开了一种基于深度学习的一对多需求分析识别方法。本方法为：1)构建一对多的深度学习神经网络模型并对其进行训练；其中，深度学习神经网络模型包括一个编码器和m个解码器；所述编码器用于计算需求文本的隐藏层状态向量并将其发送给每个解码器；解码器对输入数据依次进行处理生成一隐藏层状态向量；然后根据编码器生成的隐藏层状态向量和解码器生成的隐藏层状态向量生成一全局对齐权重向量，然后将上下文向量和隐藏层状态向量连接后输入Softmax层进行预测并输出一功能项；2)利用深度学习神经网络模型对待处理需求文本进行预测，生成m个功能项；3)合并m个功能项中相似的功能项，获得该待处理需求文本的功能项识别结果。 【EN】The invention discloses a one-to-many demand analysis and identification method based on deep learning. The method comprises the following steps: 1) constructing a one-to-many deep learning neural network model and training the model; the deep learning neural network model comprises an encoder and m decoders; the encoder is used for calculating a hidden layer state vector of the required text and sending the hidden layer state vector to each decoder; the decoder processes the input data in sequence to generate a hidden layer state vector; then generating a global alignment weight vector according to the hidden layer state vector generated by the encoder and the hidden layer state vector generated by the decoder, connecting the context vector and the hidden layer state vector, inputting the connected context vector and the hidden layer state vector into a Softmax layer for prediction and outputting a functional item; 2) predicting a to-be-processed demand text by utilizing a deep learning neural network model to generate m functional items; 3) and combining similar function items in the m function items to obtain a function item identification result of the to-be-processed requirement text.

摘要：【中文】本发明公开了一种基于语义分析和文本挖掘的事务类功能点自动识别方法及相应存储介质与电子装置，包括采集用于提取功能点的需求文本，对所述需求文本进行条目化获取第一匹配数据集，并将所述第一匹配数据集进行中文分词，得到第二匹配数据集；将提取的所述第二匹配数据集的特征送入词分类模型，得到若干功能点词语；将若干所述功能点词语组成的若干功能点短语输入语言模型，得到各功能点短语的输出概率评分值，获取所述需求文本的事务类功能点。本发明无需人工构建字典词库，无需人工配置识别规则，提出了六个维度的16个特征来刻画功能点词语及其上下文的特性，实现了事务点识别的全过程自动化，提高估算的准确性和稳定性。 【EN】The invention discloses a transaction function point automatic identification method based on semantic analysis and text mining, a corresponding storage medium and an electronic device, wherein the method comprises the steps of collecting a demand text for extracting function points, performing clauseization on the demand text to obtain a first matching data set, and performing Chinese word segmentation on the first matching data set to obtain a second matching data set; sending the extracted features of the second matching data set into a word classification model to obtain a plurality of functional point words; and inputting a plurality of function point phrases consisting of a plurality of function point words into a language model to obtain the output probability score value of each function point phrase and obtain the transaction function points of the requirement text. According to the invention, a dictionary word stock is not required to be manually constructed, and a recognition rule is not required to be manually configured, 16 characteristics of six dimensions are provided to depict the functional point words and the context thereof, so that the whole process automation of transaction point recognition is realized, and the accuracy and the stability of estimation are improved.

摘要：【中文】本发明涉及一种基于节点唯一性标识排列的区块链网络隐蔽通信方法，利用区块链节点唯一性标识在区块链业务数据中的广泛使用且具有唯一性的特点、区块链环境下数据全局可见的特性、区块链业务数据中普遍包含时间戳字段的特性，首先生成若干节点唯一性标识，按照事先约定的唯一性标识排列方式调制隐蔽信息，生成节点唯一性标识排列，之后各个隐蔽信息发送节点按照唯一性标识排列中的顺序依次发送携带隐蔽信息的区块链业务数据；隐蔽信息的接收方通过观察区块链网络，通过节点标识识别携带隐蔽信息的区块链业务数据，通过时间戳对业务数据中的节点唯一性标识排序并将其转换为节点唯一性标识排列，进而获取隐蔽信息，实现区块链网络环境下的隐蔽通信。 【EN】The invention relates to a block chain network covert communication method based on node uniqueness identifier arrangement, which utilizes the characteristics that block chain link point uniqueness identifiers are widely used in block chain service data and have uniqueness, the characteristics that data are globally visible in a block chain environment and the characteristics that a timestamp field is commonly contained in the block chain service data, firstly generates a plurality of node uniqueness identifiers, modulates covert information according to a predetermined uniqueness identifier arrangement mode, generates node uniqueness identifier arrangement, and then sequentially sends the block chain service data carrying the covert information by each covert information sending node according to the sequence in the uniqueness identifier arrangement; and a receiver of the hidden information identifies the block chain service data carrying the hidden information through a node identifier by observing the block chain network, sorts the node uniqueness identifiers in the service data through a timestamp and converts the node uniqueness identifiers into node uniqueness identifier arrangement, and then acquires the hidden information to realize hidden communication under the block chain network environment.